
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import glob
%matplotlib inline


#датафреймы с размеченными данными в среднем для песни

#df с динамическим измерением(за каждую секунду) arousal и valence
df_dynamic_arousal = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/dynamic (per second annotations)/arousal.csv', index_col='song_id')
df_dynamic_valence = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/dynamic (per second annotations)/valence.csv', index_col='song_id')

#df со статическими измерениями для всего фрагмента аудио
df_static_averaged_1_2000 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv', index_col='song_id')
df_static_averaged_2000_2058 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_2000_2058.csv',index_col='song_id')


#датафреймы с размеченными данными для каждого оценщика для песни 2.mp3

#df с динамическим измерением(за каждую секунду) arousal и valence
df_dynamic_arousal_2 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/arousal/2.csv')
df_dynamic_valence_2 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/2.csv')

#df со статическими измерениями для всего фрагмента аудио
df_static_annotations_songs_1_2000 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/song_level/static_annotations_songs_1_2000.csv')


#Посмотрим на данные для песни 2.mp3
df_dynamic_arousal_2


df_dynamic_valence_2


df_static_annotations_songs_1_2000


df_static_2 = df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId']==2]
df_static_2


#Посмотрим на распределение оценки valence для всех оценщиков
sns.boxplot(y=df_static_2[' Valence'])

<AxesSubplot:ylabel=' Valence'>


#Посмотрим на распределение оценки arousal для всех оценщиков
sns.boxplot(y=df_static_2[' Arousal'])

<AxesSubplot:ylabel=' Arousal'>


sns.boxplot(y=df_dynamic_valence_2.T.describe().T['mean'])

<AxesSubplot:ylabel='mean'>


sns.boxplot(y=df_dynamic_arousal_2.T.describe().T['mean'])

<AxesSubplot:ylabel='mean'>


#Сравним средние оценки динамические со статическими
#Нормализатор из 9-бальной шкалы в [-1,1]
def normilize(x):
    return ((x-5)/4)

valence_2 = pd.DataFrame({"static": normilize(df_static_2[' Valence']), "dynamic": df_dynamic_valence_2.T.describe().T['mean']})
valence_2.plot(kind='box', title='boxplot')

<AxesSubplot:title={'center':'boxplot'}>


arousal_2 = pd.DataFrame({"static": normilize(df_static_2[' Arousal']), "dynamic": df_dynamic_arousal_2.T.describe().T['mean']})
arousal_2.plot(kind='box', title='boxplot')

<AxesSubplot:title={'center':'boxplot'}>


fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=normilize(df_static_2[' Arousal']), y=normilize(df_static_2[' Valence']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()


fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=df_dynamic_arousal_2.T.describe().T['mean'], y=df_dynamic_valence_2.T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()


#Посмотрим на данные для песни 1000.mp3
#датафреймы с размеченными данными для каждого оценщика для песни 1010.mp3

#df с динамическим измерением(за каждую секунду) arousal и valence
df_dynamic_arousal_1010 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/arousal/1010.csv')
df_dynamic_valence_1010 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1010.csv')


df_dynamic_arousal_1010


df_dynamic_valence_1010


df_static_1010 = df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId']==1010]
df_static_1010


#Посмотрим на распределение оценки valence для всех оценщиков
sns.boxplot(y=df_static_1010[' Valence'])

<AxesSubplot:ylabel=' Valence'>


#Посмотрим на распределение оценки arousal для всех оценщиков
sns.boxplot(y=df_static_1010[' Arousal'])

<AxesSubplot:ylabel=' Arousal'>


sns.boxplot(y=df_dynamic_valence_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'])

<AxesSubplot:ylabel='mean'>


sns.boxplot(y=df_dynamic_arousal_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'])

<AxesSubplot:ylabel='mean'>


valence_1010 = pd.DataFrame({"static": normilize(df_static_1010[' Valence']), "dynamic": df_dynamic_valence_1010.drop(['WorkerId'],axis=1).T.describe().T['mean']})
valence_1010.plot(kind='box', title='boxplot')

<AxesSubplot:title={'center':'boxplot'}>


arousal_1010 = pd.DataFrame({"static": normilize(df_static_1010[' Arousal']), "dynamic": df_dynamic_arousal_1010.drop(['WorkerId'],axis=1).T.describe().T['mean']})
arousal_1010.plot(kind='box', title='boxplot')

<AxesSubplot:title={'center':'boxplot'}>


fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=normilize(df_static_1010[' Arousal']), y=normilize(df_static_1010[' Valence']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()


fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=df_dynamic_arousal_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'], y=df_dynamic_valence_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()


def find_outliers(df):
    q1=df.quantile(0.25)
    q3=df.quantile(0.75)
    IQR=q3-q1
    outliers = df[((df<(q1-1.5*IQR)) | (df>(q3+1.5*IQR)))]
    return outliers


#количество оценщиков для 2013-2014 статических измерений
len(df_static_annotations_songs_1_2000['workerID'].unique())

187


#подсчёт количества выбросов для каждого работника и для каждого статического измерения

#словарь, содержащий количество выбросов в arousal для определенного работника
dict_arousal_outliers_s = {}
#словарь, содержащий количество выбросов в valence для определенного работника
dict_valence_outliers_s = {}
dict_arousal_outliers_s = dict_arousal_outliers_s.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
dict_valence_outliers_s = dict_valence_outliers_s.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)

df_metadata_2013 = pd.read_csv('audio_data/metadata/metadata_2013.csv')
df_metadata_2014 = pd.read_csv('audio_data/metadata/metadata_2014.csv', usecols=['Id','Artist','Album','Track','Genre','segment start','segment end','last.fm labels'])

for i in df_metadata_2014['Id']:
    outliers_arousal_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Arousal'])
    for j in outliers_arousal_s.index:
        dict_arousal_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1
    outliers_valence_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Valence'])
    for j in outliers_valence_s.index:
        dict_valence_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1        

print(dict_arousal_outliers_s,'\n')
print(dict_valence_outliers_s,'\n')

dict_arousal_outliers_s_2014 = dict_arousal_outliers_s.copy()
dict_valence_outliers_s_2014 = dict_valence_outliers_s.copy()
        
for i in df_metadata_2013['song_id']:
    outliers_arousal_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Arousal'])
    for j in outliers_arousal_s.index:
        dict_arousal_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1
    outliers_valence_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Valence'])
    for j in outliers_valence_s.index:
        dict_valence_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1

print(dict_arousal_outliers_s,'\n')
print(dict_valence_outliers_s)

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#песня, которую оценил 1 работник 2 раза
df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == 323]


#подсчёт количества выбросов для каждого работника и для каждого среднего динамического измерения

#словарь, содержащий количество выбросов в arousal для определенного работника
dict_arousal_outliers_d = {}
dict_arousal_outliers_d = dict_arousal_outliers_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
#словарь, содержащий количество выбросов в valence для определенного работника
dict_valence_outliers_d = {}
dict_valence_outliers_d = dict_valence_outliers_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)

directory = 'audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)'

#в 2013 не собиралась информация по id работникам для динамических измерений
for i in df_metadata_2014['Id']:
    df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
    if ('WorkerId' in df_arousal_d.columns):
        #print(i)
        #print(df_arousal_d)
        index_series = df_arousal_d['WorkerId']
        df_arousal_d_mean = df_arousal_d.drop('WorkerId', axis=1).T.describe().T['mean']
        #print(df_arousal_d_mean)
        #print(index_series)
        df_arousal_d_mean = df_arousal_d_mean.set_axis(index_series)
        #print(df_arousal_d_mean)
        ######
        outliers_arousal_d = find_outliers(df_arousal_d_mean)
        #print(outliers_arousal_d)
        for j in outliers_arousal_d.index:
            dict_arousal_outliers_d[j]+=1
    df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
    if ('WorkerId' in df_valence_d.columns):
        #print(i)
        #print(df_valence_d)
        df_valence_d_mean = df_valence_d.drop('WorkerId', axis=1).T.describe().T['mean']
        index_series = df_valence_d['WorkerId']
        #print(df_valence_d_mean)
        #print(index_series)
        df_valence_d_mean = df_valence_d_mean.set_axis(index_series)
        #print(df_valence_d_mean)
        #####
        outliers_valence_d = find_outliers(df_valence_d_mean)
        #print(outliers_valence_d)
        for j in outliers_valence_d.index:
            dict_valence_outliers_d[j]+=1
            
print(dict_arousal_outliers_d,'\n')
print(dict_valence_outliers_d)

{'6010bbc8e7ef4b21fa38f9c3a9754ef3': 0, '3c888e77b992ae3cd2adfe16774e23b9': 0, '2afd218c3aecb6828d2be327f8b9c46f': 0, 'fd5b08ce362d855ca9152a894348130c': 0, '9c8073214a052e414811b76012df8847': 0, '6b492acb1838463dc7c07a76b26313c4': 0, '8dcd4de7be6479561269b90c1dc8a3b4': 0, 'cbc86e332e889251fe0fa71df472f202': 0, '259443e9cad56b4f68b664daa20cb323': 0, '5bf3c29fd496e2cafa9c531c7e9c6d61': 0, 'bf34e3c5724ce07d29ef12db5f767258': 0, '514278711fcdb6dfb235c03a3aa38ee3': 0, '4eb43aa7f89260bb40b3970a45a82678': 0, 'ed7ed76453bd846859f5e6b9149df276': 7, '571d66ea4abfb694d9bb1281d7701710': 0, 'd84d58533b58797bccca5811b4135c95': 0, '490951556961e4b88fe1a3ac53b3b186': 0, '2c5952c4f931410e9010eb87c276020d': 0, '0c37993dcf5f99f7b00d02495ed01bc3': 0, '64cae7b86c7dcb1b40d17e43c0c2109e': 0, '24ebcf86498b0f2793b55c5b9a7756b5': 0, '94d7a0f7c8f2e1c714b0649f835c538c': 0, 'a00de41fdf121f6dfe5db851a575d2d1': 0, '7cecbffe1da5ae974952db6c13695afe': 24, 'eca1130f44cd2e17645e40a0fa2ef59b': 32, 'd8e56509dc582df255732a0170323231': 0, 'de0b07c32bb6ca4655487026e0f3fc38': 0, '3226bc99212ad30ce2ced981de1c437a': 0, '5097e48e561bf0261b242f211b289825': 0, '9d1aa48692579ca9330d3c61d10e22e6': 0, 'ce209fb2fe01d6c85dbb29b6fd84b563': 0, '8eb1abd1acca601d1e23e85c69b1742a': 0, '7a81cab11e4b2b4aa9c0718c90835611': 0, '6ca7c27715f54e2f47ebe4e1c9dcf842': 0, 'f0e2dfdbc324f309660b731ad16ce2fd': 0, '00fa8d61b5f75e16ea71bff7a3def9c9': 0, '7685fa48defdc384f720bd1edbf17ed6': 0, 'c1edc56f4d30d94a94ee41654c4f6734': 0, 'a2a15d03117e137478c5718212805270': 0, '9ad2a06dfac81761a123cb40084dcbd7': 0, '28b56a449c99c88e1f9fff59d0f16aac': 0, 'c795c7de8f350ab35dfd1100a84bf213': 0, 'f8ecfe868f673b16e2f6e5614cb46b7d': 0, '4b4c4f319a356ea87affb872173a2095': 0, '20c7d1ee0615a09b0bac9438cc8325e2': 0, '861d48b913f37227fcf9a7b1e547c0bb': 0, 'be4e771b30940fb2d5f5dcfe3877dd2e': 0, '4480a650a96730fb629aa7fa2079e7c3': 0, 'a0f5cedc3a2371ec13663226c4b44771': 0, 'cd09cd99c38d4bfb7b554dff21196c22': 0, '1ee4bd59c9ea084016ff2811dbb00568': 0, '145b408fa103c16bb00e8734cc39a29f': 0, 'be6ec863f38f611dff4958ccd33cf638': 0, '7a23690289260d2b8c1d71d8c22b69f0': 0, '3bf93d5f7b82b46e38feb881d00c5798': 0, '1bd2dc9e0406a2e240b288685c87fa76': 0, '212246528da7c13457844fc4453e6019': 0, '84c2d6fc2e78a1d1283c386c0d4daee4': 0, 'e347f98d0a810ded7c14d7be813a5868': 0, '04db9e73677c3b8882908ef7ee4a3424': 0, 'ff18a27328ffd40ef52b7ebb7a0ded94': 0, '3c63524f15931dc8bed7a768cb7d6150': 0, '807dfc125f09be392a1f1b58c05b3742': 0, '4dfca2f4dc4e8e6c3b1b7d32e10579c1': 0, '3deba1aaf448d3857c6c70f63c2a6430': 0, '855ba3170f3e470170d23a748ccb41ed': 0, 'dd80f1940fe5b84eddde6e83542bd89b': 0, '2ec24376127909f3be36d770e6ae41a6': 0, 'b8ef6a913a63225faafd661ee2e1a7c0': 0, '34fb7d43f3a35d4afac7563b7453e31c': 0, 'f3008c23ea8f35ef870e735df67fcc46': 0, '5fa91ab06a079c59a1d9e8174a488f91': 0, '504b651e27c89d1d46d101dd87823ab6': 0, '38531641e6c0628757776b0088bcc854': 0, '807f0025a626896f04566aa37cfbce0d': 0, 'd0c51e42ea093dc9a9a98ef888637c8e': 0, 'ccfcf36a939a8af15a987fa562a49207': 0, '7975199043cec980e587889f1639c0a4': 0, 'b13fb0da6dd2f5d7fe398e6e53b52110': 0, '15ec33e862185406170ff931583b014f': 0, 'c3c21239b85dcdd6679fc212afd02a49': 0, '478905563bab736f35d94bd7b0a27f9a': 0, '2f790705ae66e70e81cc0f11ce0f4b9b': 0, '27f51a4a7fe8565d26cadb88584441e5': 0, '52b597296a8dca0c5cc5a19231eeff89': 0, '1065a2110392d3e83d61b4282f55353c': 0, '6d59196a3f495939a1a776cbebe212ba': 0, 'fa3305c1bc04047d36a8b7a4c77fa81b': 0, 'fecd63ea857606ed61cbfe2ba1a70b3b': 0, '51f5fb156a5c0051682ee1835e30fbbd': 0, 'd88c800327bffffea5562e23c276ede3': 0, '594f3fb0d194f109697627b5241f4011': 0, 'a186cdd58a92051b7c73adc9bd6e65ca': 0, '40b86bbb6927a793c05968b8b95e214d': 0, 'b37092cf23b42f8b8497d8ba89be157a': 0, '3111e02887b600ee085c72c0a3df33e8': 0, 'd8188e46705e2e87f6a051e0cb338dea': 0, '19fee46f2810f34a8b69a7768d897a59': 0, '56a384c7fb2545f9de3e85fa7445d92b': 0, 'e105c200f413d7b2c5850c0df4b9687e': 0, 'a30d244141cb2f51e0803e79bc4bd147': 69, '8ef6c1b6e31d907a3bba6f4edd303371': 0, '610d5d89f56667e3d4537c529aae496e': 1, '78b5e9744073532cc376976b5fc6b2fc': 7, 'de2b2c35312ac2f0a8510743742c0219': 66, '607f6e34a0b5923333f6b16d3a59cc98': 14, 'bb50b45a1874ede476874bd57e4cabb4': 1, 'ff9c1993d2a21f2117c30d8e295dd4ac': 138, 'b09a5957e5d5e47e556d203529a0ae6d': 28, '00de940f0b5cfc82cca4791199e3bfb3': 13, '623681f76a3eab5d9c86fbc0e1ca264b': 0, '11e5c2ba0236fe3643cac09dbdb96580': 1, '722ad4c331b7d7bebd697bfbb91ee0e8': 3, '2db8293d2a35a17f16ddf8e97122ec11': 2, 'b36ffffcd1831441a2a3b60919312ccd': 7, '6222da90667e5b0de990ce6c26dcfa15': 1, '883449004b1bf2a07a284f59ddae1fd7': 3, 'accbf566ae920d6260d28454e1ee0d6a': 5, '4c7a94e0937450ef406135849493ab99': 0, '2fb527b567d10a3ea0dac783c7c2f364': 1, '1ac10b86ba5ac18e06eea9f9954ab216': 4, '46a2289decf79f747406fa91cd92fc27': 5, '0ca907e233b8bf33bf4eca86fcbfcc8f': 0, 'd5cf45435e35cb9166f51f0a55b74a0e': 4, '62ef0e23f21039e337b545ccc4851525': 3, '93f4b8995f647ceb58689b78eb2087ef': 4, '186575d01f40537515988f1369d395eb': 0, 'd0520656367db74cb316b9a26f60864e': 1, '55016bfc877fc509861b4aba22ee4f7a': 0, 'f71b423642d3d61dffba53ddee48a7e0': 0, 'd4d0cd1ae06f54909291d34ddd6ab497': 5, 'a9f376da22eb77185afae90b192bf9ac': 0, '65794ea9f5122952403585a237bc5e52': 0, '1eb9e2c62709ff203b2d9a558abc0937': 0, 'd633894e43380403a7984858af42ecea': 1, '26bf37eb1750d163fe01ef21b0033a77': 0, '51452faff8948ad4780f9b6261e813a7': 1, 'c182e8b2f013e4e3c78cbfb01b8970c8': 2, '5862049dbaad1750964d91a98930832f': 0, '6db6afec230a8c59397b5f4d97f5da28': 3, '5bdea9864567a8614b659bf4b2132ab0': 1, '0bda2743e12d8a204ab04122658d73a3': 0, '74f10ac255db9e37db6c6cbf604eb7e0': 0, '9c3b8737a6a00626867c5e05ee80157e': 0, 'e9fda1f240982b20e0df5ba9c4a337e9': 1, '888b76dff350bdb6b25783d7c4110081': 0, 'a2a845665afabbc319eb7c5305b0a7ec': 0, '2461c19378deafd4ec26d3993eeef635': 1, 'c19c053aafbf1492b9e57ce3da5d45c1': 0, 'ca76c553e32c2f096c3bc8f7280ba408': 0, '027cefa6afc040448d29558b3175cdc1': 0, 'e336e40ae9ac101cec57e8e4323ffb79': 0, '4c2037269ef8fd0a3349e6b54dd1bb1f': 0, '248172d9efbbd5eb72d48de6884054ab': 0, 'f7502ffb3616a789e3e5b38f7325b95f': 0, '3ca50399775f0540bd002cb0d6f54a3d': 0, 'ba85e679d3c3ac42412cb8deaba66a5a': 0, '49be5653eaba26f1eb60fd9d63f23502': 0, '9edfd17e8b78d0981868f84c5f20a118': 0, '6cb1856680ea078c9f46437e2bdcd09e': 1, '651938620e6e6c78bfa7854784fe62c2': 0, 'fc2fa5656d42b49f3caf01f663085069': 0, '615d836ba25132081e0ebd2182221a59': 0, 'b6927bdee51077f7868f05f1ed39485f': 1, 'dbb8ffe292aaa9bc2de69ca511af2b3a': 0, '54cc66fe7cbb01775a6b7c7d703cdeff': 0, 'b4ba75169c585b959f1247fac7e4be7a': 0, 'd3b1de8ed2ffc3eb9af2ef40a80c7d7d': 0, '0aef6e22005035ac1b93e0a99c961f4c': 3, 'd5b7242bc8a4bb534b8489c24b81fc34': 0, '374a5659c02e12b01db6319436f17a7d': 0, '5b044cf509da1d8444b6f60c465240ef': 0, '77055e16055dc7bc817f7c4bb2eb8fae': 0, '35536d2d90b93461aa74200967d002e8': 0, '987a16808cac2496853b6f531c0878cb': 0, '80db3788bc598d1b32979bea958d9358': 0, '4dedf223de3f8ebde100df78a5428251': 0, '2a6b63b7690efa2390c8d9fee11b1407': 0, '485d8e33a731a830ef0aebd71b016d08': 0, 'b92704ea431d60d62bd8fd18e2940067': 0, 'a4410f548a05bf09b8541ec2672d8490': 0, '67785f343e6d117fac4d1697697cd8ae': 0, 'da37d1548ffd0631809f7be341e4fe4d': 0, 'ad3b997c4f2382a66e49f035cacfa682': 0, 'a34913ea1010b5812a14d1fef9586a4f': 0, '659accfd85162122ca361ac34e730c4d': 0, '9d32be9708a9520ec07e91aec9653291': 1} 

{'6010bbc8e7ef4b21fa38f9c3a9754ef3': 0, '3c888e77b992ae3cd2adfe16774e23b9': 0, '2afd218c3aecb6828d2be327f8b9c46f': 0, 'fd5b08ce362d855ca9152a894348130c': 0, '9c8073214a052e414811b76012df8847': 0, '6b492acb1838463dc7c07a76b26313c4': 0, '8dcd4de7be6479561269b90c1dc8a3b4': 0, 'cbc86e332e889251fe0fa71df472f202': 0, '259443e9cad56b4f68b664daa20cb323': 0, '5bf3c29fd496e2cafa9c531c7e9c6d61': 0, 'bf34e3c5724ce07d29ef12db5f767258': 0, '514278711fcdb6dfb235c03a3aa38ee3': 0, '4eb43aa7f89260bb40b3970a45a82678': 0, 'ed7ed76453bd846859f5e6b9149df276': 7, '571d66ea4abfb694d9bb1281d7701710': 0, 'd84d58533b58797bccca5811b4135c95': 0, '490951556961e4b88fe1a3ac53b3b186': 0, '2c5952c4f931410e9010eb87c276020d': 0, '0c37993dcf5f99f7b00d02495ed01bc3': 0, '64cae7b86c7dcb1b40d17e43c0c2109e': 0, '24ebcf86498b0f2793b55c5b9a7756b5': 0, '94d7a0f7c8f2e1c714b0649f835c538c': 0, 'a00de41fdf121f6dfe5db851a575d2d1': 0, '7cecbffe1da5ae974952db6c13695afe': 19, 'eca1130f44cd2e17645e40a0fa2ef59b': 26, 'd8e56509dc582df255732a0170323231': 0, 'de0b07c32bb6ca4655487026e0f3fc38': 0, '3226bc99212ad30ce2ced981de1c437a': 0, '5097e48e561bf0261b242f211b289825': 0, '9d1aa48692579ca9330d3c61d10e22e6': 0, 'ce209fb2fe01d6c85dbb29b6fd84b563': 0, '8eb1abd1acca601d1e23e85c69b1742a': 0, '7a81cab11e4b2b4aa9c0718c90835611': 0, '6ca7c27715f54e2f47ebe4e1c9dcf842': 0, 'f0e2dfdbc324f309660b731ad16ce2fd': 0, '00fa8d61b5f75e16ea71bff7a3def9c9': 0, '7685fa48defdc384f720bd1edbf17ed6': 0, 'c1edc56f4d30d94a94ee41654c4f6734': 0, 'a2a15d03117e137478c5718212805270': 0, '9ad2a06dfac81761a123cb40084dcbd7': 0, '28b56a449c99c88e1f9fff59d0f16aac': 0, 'c795c7de8f350ab35dfd1100a84bf213': 0, 'f8ecfe868f673b16e2f6e5614cb46b7d': 0, '4b4c4f319a356ea87affb872173a2095': 0, '20c7d1ee0615a09b0bac9438cc8325e2': 0, '861d48b913f37227fcf9a7b1e547c0bb': 0, 'be4e771b30940fb2d5f5dcfe3877dd2e': 0, '4480a650a96730fb629aa7fa2079e7c3': 0, 'a0f5cedc3a2371ec13663226c4b44771': 0, 'cd09cd99c38d4bfb7b554dff21196c22': 0, '1ee4bd59c9ea084016ff2811dbb00568': 0, '145b408fa103c16bb00e8734cc39a29f': 0, 'be6ec863f38f611dff4958ccd33cf638': 1, '7a23690289260d2b8c1d71d8c22b69f0': 0, '3bf93d5f7b82b46e38feb881d00c5798': 0, '1bd2dc9e0406a2e240b288685c87fa76': 0, '212246528da7c13457844fc4453e6019': 0, '84c2d6fc2e78a1d1283c386c0d4daee4': 0, 'e347f98d0a810ded7c14d7be813a5868': 0, '04db9e73677c3b8882908ef7ee4a3424': 0, 'ff18a27328ffd40ef52b7ebb7a0ded94': 0, '3c63524f15931dc8bed7a768cb7d6150': 0, '807dfc125f09be392a1f1b58c05b3742': 0, '4dfca2f4dc4e8e6c3b1b7d32e10579c1': 0, '3deba1aaf448d3857c6c70f63c2a6430': 0, '855ba3170f3e470170d23a748ccb41ed': 0, 'dd80f1940fe5b84eddde6e83542bd89b': 0, '2ec24376127909f3be36d770e6ae41a6': 0, 'b8ef6a913a63225faafd661ee2e1a7c0': 0, '34fb7d43f3a35d4afac7563b7453e31c': 0, 'f3008c23ea8f35ef870e735df67fcc46': 0, '5fa91ab06a079c59a1d9e8174a488f91': 0, '504b651e27c89d1d46d101dd87823ab6': 0, '38531641e6c0628757776b0088bcc854': 0, '807f0025a626896f04566aa37cfbce0d': 0, 'd0c51e42ea093dc9a9a98ef888637c8e': 0, 'ccfcf36a939a8af15a987fa562a49207': 0, '7975199043cec980e587889f1639c0a4': 0, 'b13fb0da6dd2f5d7fe398e6e53b52110': 0, '15ec33e862185406170ff931583b014f': 0, 'c3c21239b85dcdd6679fc212afd02a49': 0, '478905563bab736f35d94bd7b0a27f9a': 0, '2f790705ae66e70e81cc0f11ce0f4b9b': 0, '27f51a4a7fe8565d26cadb88584441e5': 0, '52b597296a8dca0c5cc5a19231eeff89': 0, '1065a2110392d3e83d61b4282f55353c': 0, '6d59196a3f495939a1a776cbebe212ba': 0, 'fa3305c1bc04047d36a8b7a4c77fa81b': 0, 'fecd63ea857606ed61cbfe2ba1a70b3b': 0, '51f5fb156a5c0051682ee1835e30fbbd': 0, 'd88c800327bffffea5562e23c276ede3': 0, '594f3fb0d194f109697627b5241f4011': 0, 'a186cdd58a92051b7c73adc9bd6e65ca': 0, '40b86bbb6927a793c05968b8b95e214d': 0, 'b37092cf23b42f8b8497d8ba89be157a': 0, '3111e02887b600ee085c72c0a3df33e8': 0, 'd8188e46705e2e87f6a051e0cb338dea': 0, '19fee46f2810f34a8b69a7768d897a59': 0, '56a384c7fb2545f9de3e85fa7445d92b': 0, 'e105c200f413d7b2c5850c0df4b9687e': 0, 'a30d244141cb2f51e0803e79bc4bd147': 39, '8ef6c1b6e31d907a3bba6f4edd303371': 0, '610d5d89f56667e3d4537c529aae496e': 0, '78b5e9744073532cc376976b5fc6b2fc': 3, 'de2b2c35312ac2f0a8510743742c0219': 68, '607f6e34a0b5923333f6b16d3a59cc98': 22, 'bb50b45a1874ede476874bd57e4cabb4': 9, 'ff9c1993d2a21f2117c30d8e295dd4ac': 83, 'b09a5957e5d5e47e556d203529a0ae6d': 29, '00de940f0b5cfc82cca4791199e3bfb3': 8, '623681f76a3eab5d9c86fbc0e1ca264b': 0, '11e5c2ba0236fe3643cac09dbdb96580': 0, '722ad4c331b7d7bebd697bfbb91ee0e8': 1, '2db8293d2a35a17f16ddf8e97122ec11': 7, 'b36ffffcd1831441a2a3b60919312ccd': 6, '6222da90667e5b0de990ce6c26dcfa15': 0, '883449004b1bf2a07a284f59ddae1fd7': 12, 'accbf566ae920d6260d28454e1ee0d6a': 12, '4c7a94e0937450ef406135849493ab99': 0, '2fb527b567d10a3ea0dac783c7c2f364': 0, '1ac10b86ba5ac18e06eea9f9954ab216': 8, '46a2289decf79f747406fa91cd92fc27': 5, '0ca907e233b8bf33bf4eca86fcbfcc8f': 1, 'd5cf45435e35cb9166f51f0a55b74a0e': 3, '62ef0e23f21039e337b545ccc4851525': 0, '93f4b8995f647ceb58689b78eb2087ef': 2, '186575d01f40537515988f1369d395eb': 0, 'd0520656367db74cb316b9a26f60864e': 0, '55016bfc877fc509861b4aba22ee4f7a': 0, 'f71b423642d3d61dffba53ddee48a7e0': 0, 'd4d0cd1ae06f54909291d34ddd6ab497': 2, 'a9f376da22eb77185afae90b192bf9ac': 0, '65794ea9f5122952403585a237bc5e52': 0, '1eb9e2c62709ff203b2d9a558abc0937': 1, 'd633894e43380403a7984858af42ecea': 0, '26bf37eb1750d163fe01ef21b0033a77': 0, '51452faff8948ad4780f9b6261e813a7': 1, 'c182e8b2f013e4e3c78cbfb01b8970c8': 1, '5862049dbaad1750964d91a98930832f': 0, '6db6afec230a8c59397b5f4d97f5da28': 2, '5bdea9864567a8614b659bf4b2132ab0': 7, '0bda2743e12d8a204ab04122658d73a3': 0, '74f10ac255db9e37db6c6cbf604eb7e0': 0, '9c3b8737a6a00626867c5e05ee80157e': 0, 'e9fda1f240982b20e0df5ba9c4a337e9': 0, '888b76dff350bdb6b25783d7c4110081': 0, 'a2a845665afabbc319eb7c5305b0a7ec': 1, '2461c19378deafd4ec26d3993eeef635': 0, 'c19c053aafbf1492b9e57ce3da5d45c1': 2, 'ca76c553e32c2f096c3bc8f7280ba408': 0, 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'80db3788bc598d1b32979bea958d9358': 0, '4dedf223de3f8ebde100df78a5428251': 0, '2a6b63b7690efa2390c8d9fee11b1407': 0, '485d8e33a731a830ef0aebd71b016d08': 0, 'b92704ea431d60d62bd8fd18e2940067': 1, 'a4410f548a05bf09b8541ec2672d8490': 0, '67785f343e6d117fac4d1697697cd8ae': 0, 'da37d1548ffd0631809f7be341e4fe4d': 0, 'ad3b997c4f2382a66e49f035cacfa682': 0, 'a34913ea1010b5812a14d1fef9586a4f': 1, '659accfd85162122ca361ac34e730c4d': 0, '9d32be9708a9520ec07e91aec9653291': 0}


#сравнение количества выбросов в arousal в статике и динамике в 2014 году
plt.bar(['static_arousal_2014','dynamic_arousal_2014'], [sum(dict_arousal_outliers_s_2014.values()),sum(dict_arousal_outliers_d.values())])
plt.ylabel('количество выбросов')
plt.show()


#сравнение количества выбросов в valence в статике и динамике в 2014 году
plt.bar(['static_valence_2014','dynamic_valence_2014'], [sum(dict_valence_outliers_s_2014.values()),sum(dict_valence_outliers_d.values())])
plt.ylabel('количество выбросов')
plt.show()


#топ-10 работников по количеству выбросов в static_valence
print('Количество работников всего =', len(dict_valence_outliers_s))
print('Количество работников с 0 outlier =', len([k for k, v in dict_valence_outliers_s.items() if v==0]))
print([i[1] for i in sorted(dict_valence_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_valence_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_valence_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()

Количество работников всего = 187
Количество работников с 0 outlier = 91
[182, 108, 35, 34, 31, 28, 26, 24, 24, 20]


#топ-10 работников по количеству выбросов в static_arousal
print('Количество работников всего =', len(dict_arousal_outliers_s))
print('Количество работников с 0 outlier =', len([k for k, v in dict_arousal_outliers_s.items() if v==0]))
print([i[1] for i in sorted(dict_arousal_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_arousal_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_arousal_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()

Количество работников всего = 187
Количество работников с 0 outlier = 99
[136, 107, 74, 53, 40, 33, 33, 30, 29, 27]


#топ-10 работников по количеству выбросов в  dynamic_valence
print('Количество работников всего =', len(dict_valence_outliers_d))
print('Количество работников с 0 outlier =', len([k for k, v in dict_valence_outliers_d.items() if v==0]))
print([i[1] for i in sorted(dict_valence_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_valence_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_valence_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()

Количество работников всего = 187
Количество работников с 0 outlier = 153
[83, 68, 39, 29, 26, 22, 19, 12, 12, 9]


#топ-10 работников по количеству выбросов в dynamic_arousal
print('Количество работников всего =', len(dict_arousal_outliers_d))
print('Количество работников с 0 outlier =', len([k for k, v in dict_arousal_outliers_d.items() if v==0]))
print([i[1] for i in sorted(dict_arousal_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_arousal_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_arousal_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()

Количество работников всего = 187
Количество работников с 0 outlier = 149
[138, 69, 66, 32, 28, 24, 14, 13, 7, 7]


#Диаграмма, содержащая долю выбросов в данных 2014 года на каждого работника
fig, ax = plt.subplots(figsize=(11,11))
dict_all_outliers = {k : dict_arousal_outliers_d.get(k,0)+dict_arousal_outliers_s.get(k,0)+dict_valence_outliers_d.get(k,0)+dict_valence_outliers_s.get(k,0) for k in set(dict_valence_outliers_s)|set(dict_valence_outliers_d)|set(dict_arousal_outliers_s)|set(dict_arousal_outliers_d)}
print([i[1] for i in sorted(dict_all_outliers.items(), key=lambda item: item[1], reverse=True)])
plt.pie([i[1] for i in sorted(dict_all_outliers.items(), key=lambda item: item[1], reverse=True)],labels=[i[0] for i in sorted(dict_all_outliers.items(), key=lambda item: item[1], reverse=True)],autopct=lambda p: format(p, '.2f') if p > 2.5 else None)
plt.show()

[423, 253, 244, 210, 108, 102, 89, 87, 79, 75, 70, 51, 45, 40, 39, 37, 33, 32, 32, 31, 30, 26, 26, 26, 23, 22, 20, 18, 17, 17, 16, 14, 13, 11, 10, 10, 10, 10, 9, 9, 8, 8, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]


#данные 2015 статика
df_static_annotations_songs_2000_2058 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/song_level/static_annotations_songs_2000_2058.csv')
df_static_annotations_songs_2000_2058.head(5)


#данные 2015 динамика для 2001 песни
df_dynamic_valence_2001 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/2001.csv')
df_dynamic_valence_2001


#количество измерений который сделал каждый работник в 2013 и 2014 статика
dict_workers_freq_s = {}
dict_workers_freq_s = dict_workers_freq_s.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
for i in df_static_annotations_songs_1_2000.index:
    dict_workers_freq_s[df_static_annotations_songs_1_2000.loc[i]['workerID']]+=1
dict_workers_freq_s

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 'ca76c553e32c2f096c3bc8f7280ba408': 3,
 '027cefa6afc040448d29558b3175cdc1': 9,
 'e336e40ae9ac101cec57e8e4323ffb79': 6,
 '4c2037269ef8fd0a3349e6b54dd1bb1f': 9,
 '248172d9efbbd5eb72d48de6884054ab': 24,
 'f7502ffb3616a789e3e5b38f7325b95f': 12,
 '3ca50399775f0540bd002cb0d6f54a3d': 6,
 'ba85e679d3c3ac42412cb8deaba66a5a': 3,
 '49be5653eaba26f1eb60fd9d63f23502': 3,
 '9edfd17e8b78d0981868f84c5f20a118': 3,
 '6cb1856680ea078c9f46437e2bdcd09e': 6,
 '651938620e6e6c78bfa7854784fe62c2': 3,
 'fc2fa5656d42b49f3caf01f663085069': 6,
 '615d836ba25132081e0ebd2182221a59': 6,
 'b6927bdee51077f7868f05f1ed39485f': 18,
 'dbb8ffe292aaa9bc2de69ca511af2b3a': 3,
 '54cc66fe7cbb01775a6b7c7d703cdeff': 3,
 'b4ba75169c585b959f1247fac7e4be7a': 3,
 'd3b1de8ed2ffc3eb9af2ef40a80c7d7d': 3,
 '0aef6e22005035ac1b93e0a99c961f4c': 6,
 'd5b7242bc8a4bb534b8489c24b81fc34': 3,
 '374a5659c02e12b01db6319436f17a7d': 3,
 '5b044cf509da1d8444b6f60c465240ef': 3,
 '77055e16055dc7bc817f7c4bb2eb8fae': 6,
 '35536d2d90b93461aa74200967d002e8': 3,
 '987a16808cac2496853b6f531c0878cb': 3,
 '80db3788bc598d1b32979bea958d9358': 6,
 '4dedf223de3f8ebde100df78a5428251': 3,
 '2a6b63b7690efa2390c8d9fee11b1407': 3,
 '485d8e33a731a830ef0aebd71b016d08': 6,
 'b92704ea431d60d62bd8fd18e2940067': 6,
 'a4410f548a05bf09b8541ec2672d8490': 3,
 '67785f343e6d117fac4d1697697cd8ae': 3,
 'da37d1548ffd0631809f7be341e4fe4d': 3,
 'ad3b997c4f2382a66e49f035cacfa682': 3,
 'a34913ea1010b5812a14d1fef9586a4f': 3,
 '659accfd85162122ca361ac34e730c4d': 3,
 '9d32be9708a9520ec07e91aec9653291': 3}


#связь частоты и количества выбросов в 2013 и 2014 статика
fig, ax = plt.subplots(figsize=(6,6))
dict_all_outliers_s = {k : dict_arousal_outliers_s.get(k,0)+dict_valence_outliers_s.get(k,0) for k in set(dict_valence_outliers_s)|set(dict_arousal_outliers_s)}
array_freq =[]
array_outliers =[]
for k,v in dict_all_outliers_s.items():
    array_freq.append(dict_workers_freq_s[k])
    array_outliers.append(dict_all_outliers_s[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
plt.show()


#частота работников в arousal и valence 2014 динамика
dict_worker_freq_ar_d={}
dict_worker_freq_val_d={}
dict_worker_freq_ar_d=dict_worker_freq_ar_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
dict_worker_freq_val_d=dict_worker_freq_val_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)

for i in df_metadata_2014['Id']:
    df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
    if ('WorkerId' in df_arousal_d.columns):
        for j in df_arousal_d['WorkerId']:
            dict_worker_freq_ar_d[j]+=1
    df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
    if ('WorkerId' in df_valence_d.columns):
        for j in df_valence_d['WorkerId']:
            dict_worker_freq_val_d[j]+=1


#связь частоты и количества выбросов в 2014 динамика для arousal
fig, ax = plt.subplots(figsize=(6,6))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_outliers_d.items():
    array_freq.append(dict_worker_freq_ar_d[k])
    array_outliers.append(dict_arousal_outliers_d[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2014 динамика для valence
fig, ax = plt.subplots(figsize=(6,6))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_outliers_d.items():
    array_freq.append(dict_worker_freq_val_d[k])
    array_outliers.append(dict_valence_outliers_d[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2014 динамика

array_freq =[]
array_outliers =[]
for k,v in dict_arousal_outliers_d.items():
    array_freq.append(dict_worker_freq_ar_d[k])
    array_outliers.append(dict_arousal_outliers_d[k])
plt.scatter(x=array_freq,y=array_outliers,color='b')
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])

array_freq_2 = []
array_outliers_2 =[]
for k,v in dict_valence_outliers_d.items():
    array_freq_2.append(dict_worker_freq_val_d[k])
    array_outliers_2.append(dict_valence_outliers_d[k])
array_freq = array_freq + array_freq_2
array_outliers = array_outliers+array_outliers_2
plt.scatter(x=array_freq,y=array_outliers,color='b')
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print(array_freq)
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#анализ выбросов для данных 2015

df_metadata_2015 = pd.read_csv('audio_data/metadata/metadata_2015.csv',usecols=['id','Filename','title','artist','album','genre'])
df_metadata_2015.head()


#все работники оценивающие динамику
directory = 'audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)'
workers_arousal = pd.Series()
workers_valence = pd.Series()
for i in df_metadata_2015['id']:
    df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
    workers_arousal = pd.concat([workers_arousal,df_arousal_d['WorkerId']], axis=0)
    df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
    workers_valence = pd.concat([workers_valence,df_valence_d['WorkerId']], axis=0)
workers_arousal_2015 = workers_arousal.unique()
workers_valence_2015 = workers_valence.unique()
dict_worker_freq_ar_d_2015 = {}
dict_worker_freq_ar_d_2015 = dict_worker_freq_ar_d_2015.fromkeys(workers_arousal_2015, 0)
dict_worker_freq_val_d_2015 = {}
dict_worker_freq_val_d_2015 = dict_worker_freq_val_d_2015.fromkeys(workers_valence_2015, 0)
for i in df_metadata_2015['id']:
    df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
    for j in df_arousal_d['WorkerId']:
        dict_worker_freq_ar_d_2015[j]+=1
    df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
    for j in df_valence_d['WorkerId']:
        dict_worker_freq_val_d_2015[j]+=1

C:\Users\0D04~1\AppData\Local\Temp/ipykernel_20668/64907381.py:3: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  workers_arousal = pd.Series()
C:\Users\0D04~1\AppData\Local\Temp/ipykernel_20668/64907381.py:4: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  workers_valence = pd.Series()


#подсчитывание выбросов для динамики arousal и valence
dict_arousal_outliers_d_2015 = {}
dict_arousal_outliers_d_2015 = dict_arousal_outliers_d_2015.fromkeys(workers_arousal_2015, 0)
dict_valence_outliers_d_2015 = {}
dict_valence_outliers_d_2015 = dict_valence_outliers_d_2015.fromkeys(workers_valence_2015, 0)

directory = 'audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)'

for i in df_metadata_2015['id']:
    df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
    #print(i)
    #print(df_arousal_d)
    index_series = df_arousal_d['WorkerId']
    df_arousal_d_mean = df_arousal_d.drop('WorkerId', axis=1).T.describe().T['mean']
    #print(df_arousal_d_mean)
    #print(index_series)
    df_arousal_d_mean = df_arousal_d_mean.set_axis(index_series)
    #print(df_arousal_d_mean)
    ######
    outliers_arousal_d = find_outliers(df_arousal_d_mean)
    #print(outliers_arousal_d)
    for j in outliers_arousal_d.index:
        dict_arousal_outliers_d_2015[j]+=1
    df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
    #print(i)
    #print(df_valence_d)
    df_valence_d_mean = df_valence_d.drop('WorkerId', axis=1).T.describe().T['mean']
    index_series = df_valence_d['WorkerId']
    #print(df_valence_d_mean)
    #print(index_series)
    df_valence_d_mean = df_valence_d_mean.set_axis(index_series)
    #print(df_valence_d_mean)
    #####
    outliers_valence_d = find_outliers(df_valence_d_mean)
    #print(outliers_valence_d)
    for j in outliers_valence_d.index:
        dict_valence_outliers_d_2015[j]+=1
        
print(dict_arousal_outliers_d_2015,'\n')
print(dict_valence_outliers_d_2015)

{'883449004b1bf2a07a284f59ddae1fd7': 1, 'b09a5957e5d5e47e556d203529a0ae6d': 18, 'a8d14f76676af36b8978406be47c0c38': 5, '02bf7a99a5e47f4d52939ab7efc8a549': 2, '467b1ff3ccfff51e2882bd9d39ef2082': 0, 'e692af6e00f4a7d14933fd23058998ca': 2, '4874e802dab1e5f13ee4da6403f40d68': 1, '5c71ac012aadbd3491d3cc2a6b52afaa': 1} 

{'883449004b1bf2a07a284f59ddae1fd7': 3, 'b09a5957e5d5e47e556d203529a0ae6d': 4, 'a8d14f76676af36b8978406be47c0c38': 2, '02bf7a99a5e47f4d52939ab7efc8a549': 1, '467b1ff3ccfff51e2882bd9d39ef2082': 0, 'e692af6e00f4a7d14933fd23058998ca': 4, '4874e802dab1e5f13ee4da6403f40d68': 4}


#связь частоты и количества выбросов в 2015 динамика для arousal
fig, ax = plt.subplots(figsize=(10,10))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_outliers_d_2015.items():
    array_freq.append(dict_worker_freq_ar_d_2015[k])
    array_outliers.append(dict_arousal_outliers_d_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2015 динамика для valence
fig, ax = plt.subplots(figsize=(10,10))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_outliers_d_2015.items():
    array_freq.append(dict_worker_freq_val_d_2015[k])
    array_outliers.append(dict_valence_outliers_d_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#все работники оценивающие статику
workers_static = df_static_annotations_songs_2000_2058[' WorkerId'].unique()
workers_static

array(['523edc3414996c8e52eb14f20c93fe96',
       'ae0b751438f08bb474e5cd3e48e0d417',
       'fd4cc3ae7434a18286204f133b490502',
       '58af5138dd2f00af1f52d2c2c031f4a2',
       '3c960e4e5a02213fc82b98b770a0663c',
       '949fcd0caf9d4b51a280acfbcd5e8e80',
       'b8bc7171b9ff0de8aaed0bf210fb711c',
       'b4cad77b1093f7d134c6c141af4dddad'], dtype=object)


#подсчёт выбросов для данных 2015 года
dict_arousal_av_outliers_s_2015 = {}
dict_valence_av_outliers_s_2015 = {}
dict_arousal_max_outliers_s_2015 = {}
dict_valence_max_outliers_s_2015 = {}
dict_arousal_min_outliers_s_2015 = {}
dict_valence_min_outliers_s_2015 = {}
dict_arousal_av_outliers_s_2015 = dict_arousal_av_outliers_s_2015.fromkeys(workers_static, 0)
dict_valence_av_outliers_s_2015 = dict_valence_av_outliers_s_2015.fromkeys(workers_static, 0)
dict_arousal_max_outliers_s_2015 = dict_arousal_max_outliers_s_2015.fromkeys(workers_static, 0)
dict_valence_max_outliers_s_2015 = dict_valence_max_outliers_s_2015.fromkeys(workers_static, 0)
dict_arousal_min_outliers_s_2015 = dict_arousal_min_outliers_s_2015.fromkeys(workers_static, 0)
dict_valence_min_outliers_s_2015 = dict_valence_min_outliers_s_2015.fromkeys(workers_static, 0)

for i in df_metadata_2015['id']:
    outliers_arousal_av_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Arousal_Average'])
    for j in outliers_arousal_av_outliers_s_2015.index:
        dict_arousal_av_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
    outliers_valence_av_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Valence_Average'])
    for j in outliers_valence_av_outliers_s_2015.index:
        dict_valence_av_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
    outliers_arousal_max_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Arousal_Maximum'])
    for j in outliers_arousal_max_outliers_s_2015.index:
        dict_arousal_max_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
    outliers_valence_max_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Valence_Maximum'])
    for j in outliers_valence_max_outliers_s_2015.index:
        dict_valence_max_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
    outliers_arousal_min_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Arousal_Minimum'])
    for j in outliers_arousal_min_outliers_s_2015.index:
        dict_arousal_min_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
    outliers_valence_min_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Valence_Minimum'])
    for j in outliers_valence_min_outliers_s_2015.index:
        dict_valence_min_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1

print(dict_arousal_av_outliers_s_2015,'\n')
print(dict_valence_av_outliers_s_2015,'\n')
print(dict_arousal_max_outliers_s_2015,'\n')
print(dict_valence_max_outliers_s_2015,'\n')
print(dict_arousal_min_outliers_s_2015,'\n')
print(dict_valence_min_outliers_s_2015)

{'523edc3414996c8e52eb14f20c93fe96': 6, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 5, '58af5138dd2f00af1f52d2c2c031f4a2': 1, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 1, 'b8bc7171b9ff0de8aaed0bf210fb711c': 0, 'b4cad77b1093f7d134c6c141af4dddad': 0} 

{'523edc3414996c8e52eb14f20c93fe96': 2, 'ae0b751438f08bb474e5cd3e48e0d417': 4, 'fd4cc3ae7434a18286204f133b490502': 3, '58af5138dd2f00af1f52d2c2c031f4a2': 3, '3c960e4e5a02213fc82b98b770a0663c': 1, '949fcd0caf9d4b51a280acfbcd5e8e80': 5, 'b8bc7171b9ff0de8aaed0bf210fb711c': 4, 'b4cad77b1093f7d134c6c141af4dddad': 0} 

{'523edc3414996c8e52eb14f20c93fe96': 2, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 0, '58af5138dd2f00af1f52d2c2c031f4a2': 1, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 1, 'b8bc7171b9ff0de8aaed0bf210fb711c': 0, 'b4cad77b1093f7d134c6c141af4dddad': 0} 

{'523edc3414996c8e52eb14f20c93fe96': 0, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 5, '58af5138dd2f00af1f52d2c2c031f4a2': 5, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 6, 'b8bc7171b9ff0de8aaed0bf210fb711c': 5, 'b4cad77b1093f7d134c6c141af4dddad': 0} 

{'523edc3414996c8e52eb14f20c93fe96': 5, 'ae0b751438f08bb474e5cd3e48e0d417': 1, 'fd4cc3ae7434a18286204f133b490502': 6, '58af5138dd2f00af1f52d2c2c031f4a2': 1, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 4, 'b8bc7171b9ff0de8aaed0bf210fb711c': 0, 'b4cad77b1093f7d134c6c141af4dddad': 0} 

{'523edc3414996c8e52eb14f20c93fe96': 3, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 1, '58af5138dd2f00af1f52d2c2c031f4a2': 7, '3c960e4e5a02213fc82b98b770a0663c': 1, '949fcd0caf9d4b51a280acfbcd5e8e80': 2, 'b8bc7171b9ff0de8aaed0bf210fb711c': 5, 'b4cad77b1093f7d134c6c141af4dddad': 0}


#количество измерений работников
dict_worker_freq_s_2015 = {}
dict_worker_freq_s_2015 = dict_worker_freq_s_2015.fromkeys(workers_static, 0)
for i in df_static_annotations_songs_2000_2058.index:
    dict_worker_freq_s_2015[df_static_annotations_songs_2000_2058.loc[i][' WorkerId']]+=1
print(dict_worker_freq_s_2015)

{'523edc3414996c8e52eb14f20c93fe96': 58, 'ae0b751438f08bb474e5cd3e48e0d417': 59, 'fd4cc3ae7434a18286204f133b490502': 58, '58af5138dd2f00af1f52d2c2c031f4a2': 36, '3c960e4e5a02213fc82b98b770a0663c': 2, '949fcd0caf9d4b51a280acfbcd5e8e80': 50, 'b8bc7171b9ff0de8aaed0bf210fb711c': 34, 'b4cad77b1093f7d134c6c141af4dddad': 1}


#связь частоты и количества выбросов в 2015 статика для arousal_average
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_av_outliers_s_2015.items():
    array_freq.append(dict_worker_freq_s_2015[k])
    array_outliers.append(dict_arousal_av_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2015 статика для valence_average
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_av_outliers_s_2015.items():
    array_freq.append(dict_worker_freq_s_2015[k])
    array_outliers.append(dict_valence_av_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2015 статика для arousal_maximum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_max_outliers_s_2015.items():
    array_freq.append(dict_worker_freq_s_2015[k])
    array_outliers.append(dict_arousal_max_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2015 статика для valence_maximum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_max_outliers_s_2015.items():
    array_freq.append(dict_worker_freq_s_2015[k])
    array_outliers.append(dict_valence_max_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2015 статика для arousal_minimum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_min_outliers_s_2015.items():
    array_freq.append(dict_worker_freq_s_2015[k])
    array_outliers.append(dict_arousal_min_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#связь частоты и количества выбросов в 2015 статика для valence_minimum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_min_outliers_s_2015.items():
    array_freq.append(dict_worker_freq_s_2015[k])
    array_outliers.append(dict_valence_min_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()


#сравнение количества выбросов в arousal в статике и динамике в 2015
plt.bar(['static_arousal_2015','dynamic_arousal_2015'], [sum(dict_arousal_av_outliers_s_2015.values()),sum(dict_arousal_outliers_d_2015.values())])
plt.show()


#сравнение количества выбросов в valence в статике и динамике в 2015
plt.bar(['static_valence_2015','dynamic_valence_2015'], [sum(dict_valence_av_outliers_s_2015.values()),sum(dict_valence_outliers_d_2015.values())])
plt.show()


#график всех песен в 2013-2014 годах построенный по arousal и valence статика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=normilize(df_static_averaged_1_2000[' arousal_mean']),x=normilize(df_static_averaged_1_2000[' valence_mean']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()

dict_quarters_s ={1:0,2:0,3:0,4:0}

for i in df_static_averaged_1_2000.index:
    arousal = normilize(df_static_averaged_1_2000.loc[i][' arousal_mean'])
    valence = normilize(df_static_averaged_1_2000.loc[i][' valence_mean'])
    #print(arousal, valence)
    if ((arousal>0) and (valence>0)):
        dict_quarters_s[1]+=1
    if ((arousal>0) and (valence<=0)):
        dict_quarters_s[2]+=1
    if ((arousal<=0) and (valence<=0)):
        dict_quarters_s[3]+=1
    if ((arousal<=0) and (valence>0)):
        dict_quarters_s[4]+=1
        
print(dict_quarters_s)

{1: 583, 2: 217, 3: 723, 4: 221}


#график всех песен в 2013-2014 годах построенный по arousal и valence динамика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=df_dynamic_arousal.loc[1:2000].T.describe().T['mean'], x=df_dynamic_valence.loc[1:2000].T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()

dict_quarters_d={1:0,2:0,3:0,4:0}

for i in set(df_dynamic_arousal.loc[1:2000].index):
    arousal = df_dynamic_arousal.loc[i].T.describe().T['mean']
    valence = df_dynamic_valence.loc[i].T.describe().T['mean']
    #print(arousal, valence)
    if ((arousal>0) and (valence>0)):
        dict_quarters_d[1]+=1
    if ((arousal>0) and (valence<=0)):
        dict_quarters_d[2]+=1
    if ((arousal<=0) and (valence<=0)):
        dict_quarters_d[3]+=1
    if ((arousal<=0) and (valence>0)):
        dict_quarters_d[4]+=1
        
print(dict_quarters_d)

{1: 954, 2: 220, 3: 374, 4: 196}


#график всех песен в 2015 построенный по arousal и valence статика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=normilize(df_static_averaged_2000_2058[' arousal_mean']), x=normilize(df_static_averaged_2000_2058[' valence_mean']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()

dict_quarters_s_2015 ={1:0,2:0,3:0,4:0}

for i in df_static_averaged_2000_2058.index:
    arousal = normilize(df_static_averaged_2000_2058.loc[i][' arousal_mean'])
    valence = normilize(df_static_averaged_2000_2058.loc[i][' valence_mean'])
    #print(arousal, valence)
    if ((arousal>0) and (valence>0)):
        dict_quarters_s_2015[1]+=1
    if ((arousal>0) and (valence<=0)):
        dict_quarters_s_2015[2]+=1
    if ((arousal<=0) and (valence<=0)):
        dict_quarters_s_2015[3]+=1
    if ((arousal<=0) and (valence>0)):
        dict_quarters_s_2015[4]+=1
        
print(dict_quarters_s_2015)

{1: 11, 2: 12, 3: 19, 4: 16}


#график всех песен в 2015 построенный по arousal и valence динамика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)

ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=df_dynamic_arousal.loc[2000:2058].T.describe().T['mean'], x=df_dynamic_valence.loc[2000:2058].T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()

dict_quarters_d_2015={1:0,2:0,3:0,4:0}

for i in set(df_dynamic_arousal.loc[2000:2058].index):
    arousal = df_dynamic_arousal.loc[i].T.describe().T['mean']
    valence = df_dynamic_valence.loc[i].T.describe().T['mean']
    #print(arousal, valence)
    if ((arousal>0) and (valence>0)):
        dict_quarters_d_2015[1]+=1
    if ((arousal>0) and (valence<=0)):
        dict_quarters_d_2015[2]+=1
    if ((arousal<=0) and (valence<=0)):
        dict_quarters_d_2015[3]+=1
    if ((arousal<=0) and (valence>0)):
        dict_quarters_d_2015[4]+=1
        
print(dict_quarters_d_2015)

{1: 23, 2: 16, 3: 11, 4: 9}


#сравнение количества песен, попавших в одну четверть, 2013-2014
X=dict_quarters_s.keys()

X_axis = np.arange(len(X)) 
  
plt.bar(X_axis - 0.2, dict_quarters_s.values(), 0.4, label = 'static') 
plt.bar(X_axis + 0.2, dict_quarters_d.values(), 0.4, label = 'dynamic')
plt.xticks(X_axis, X) 
plt.legend() 
plt.show()


#сравнение количества песен, попавших в одну четверть, 2015
X=dict_quarters_s.keys()

X_axis = np.arange(len(X)) 
  
plt.bar(X_axis - 0.2, dict_quarters_s_2015.values(), 0.4, label = 'static') 
plt.bar(X_axis + 0.2, dict_quarters_d_2015.values(), 0.4, label = 'dynamic')
plt.xticks(X_axis, X) 
plt.legend() 
plt.show()


#связь valence_dynamic и valence_static
fig, ax = plt.subplots(figsize=(5,5))
X = df_dynamic_valence.T.describe().T['mean']
Y = pd.concat([df_static_averaged_1_2000[' valence_mean'],df_static_averaged_2000_2058[' valence_mean']])
plt.scatter(X,Y)
plt.xlabel('valence_dynamic')
plt.ylabel('valence_static')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(X, Y, 1)
plt.plot(np.array(X),m*np.array(X)+b,color='r')
plt.show()


#связь arousal_dynamic и arousal_static
fig, ax = plt.subplots(figsize=(5,5))
X = df_dynamic_arousal.T.describe().T['mean']
Y = pd.concat([df_static_averaged_1_2000[' arousal_mean'],df_static_averaged_2000_2058[' arousal_mean']])
plt.scatter(X,Y)
plt.xlabel('arousal_dynamic')
plt.ylabel('arousal_static')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(X, Y, 1)
plt.plot(np.array(X),m*np.array(X)+b,color='r')
plt.show()


#сравнение количества песен, попавших в одну четверть
X=dict_quarters_s.keys()

X_axis = np.arange(len(X)) 

dict_quarters_s_all = {k : dict_quarters_s.get(k,0)+dict_quarters_s_2015.get(k,0) for k in set(dict_quarters_s)}
dict_quarters_d_all = {k : dict_quarters_d.get(k,0)+dict_quarters_d_2015.get(k,0) for k in set(dict_quarters_d)}

plt.bar(X_axis - 0.2, dict_quarters_s_all.values(), 0.4, label = 'static') 
plt.bar(X_axis + 0.2, dict_quarters_d_all.values(), 0.4, label = 'dynamic')
plt.xticks(X_axis, X) 
plt.legend() 
plt.show()


#статистическое распределение попаданий в четверти
plt.pie(dict_quarters_s_all.values(),labels=dict_quarters_s_all.keys(),autopct= '%1.1f%%')
plt.show()


#динамическое распределение попаданий в четверти
plt.pie(dict_quarters_d_all.values(),labels=dict_quarters_d_all.keys(),autopct= '%1.1f%%')
plt.show()


#создаем столбец с индексом класса
def index_class(arousal, valence):
    if ((arousal>0) and (valence>0)):
        return 1
    if ((arousal>0) and (valence<=0)):
        return 2
    if ((arousal<=0) and (valence<=0)):
        return 3
    if ((arousal<=0) and (valence>0)):
        return 4
    
indexes_class_1_2000 = [index_class(normilize(df_static_averaged_1_2000[' arousal_mean'].loc[i]), normilize(df_static_averaged_1_2000[' valence_mean'].loc[i])) for i in df_static_averaged_1_2000.index]
df_static_averaged_1_2000["class"] = indexes_class_1_2000
indexes_class_2000_2058 = [index_class(normilize(df_static_averaged_2000_2058[' arousal_mean'].loc[i]), normilize(df_static_averaged_2000_2058[' valence_mean'].loc[i])) for i in df_static_averaged_2000_2058.index]
df_static_averaged_2000_2058["class"] = indexes_class_2000_2058


df_static_averaged_1_2000.head()


df_static_averaged_2000_2058.head()


#!pip install librosa


#!pip3 install pydub


#!pip install ffmpeg-python


import IPython.display as ipd
import librosa
import librosa.display
from pydub import AudioSegment


### 1 class song 7.mp3

sound = AudioSegment.from_mp3('audio_data/DEAM_audio/MEMD_audio/7.mp3')
sound.export('audio_data/DEAM_audio/7.wav', format='wav')

filename ='audio_data/DEAM_audio/7.wav'
plt.figure(figsize=(14,5))
song7,sample_rate=librosa.load(filename)
librosa.display.waveshow(song7,sr=sample_rate)
ipd.Audio(filename)


###2 class song 1996.mp3

sound = AudioSegment.from_mp3('audio_data/DEAM_audio/MEMD_audio/1996.mp3')
sound.export('audio_data/DEAM_audio/1996.wav', format='wav')

filename ='audio_data/DEAM_audio/1996.wav'
plt.figure(figsize=(14,5))
song1996,sample_rate=librosa.load(filename)
librosa.display.waveshow(song1996,sr=sample_rate)
ipd.Audio(filename)


###3 class song 2.mp3

sound = AudioSegment.from_mp3('audio_data/DEAM_audio/MEMD_audio/2.mp3')
sound.export('audio_data/DEAM_audio/2.wav', format='wav')

filename ='audio_data/DEAM_audio/2.wav'
plt.figure(figsize=(14,5))
song2,sample_rate=librosa.load(filename)
librosa.display.waveshow(song2,sr=sample_rate)
ipd.Audio(filename)


###4 class song 19.mp3

sound = AudioSegment.from_mp3('audio_data/DEAM_audio/MEMD_audio/19.mp3')
sound.export('audio_data/DEAM_audio/19.wav', format='wav')

filename ='audio_data/DEAM_audio/19.wav'
plt.figure(figsize=(14,5))
song19,sample_rate=librosa.load(filename)
librosa.display.waveshow(song19,sr=sample_rate)
ipd.Audio(filename)


#спектрограмма для 7.mp3
song7_stft = librosa.stft(song7)
song7_stft_energy = np.abs(song7_stft) ** 2
plt.figure(figsize=(25, 10))
librosa.display.specshow(song7_stft_energy, sr=sample_rate, x_axis="time", y_axis='linear')
plt.colorbar(format="%+2.f")

<matplotlib.colorbar.Colorbar at 0x2501e7d0c70>


#mel спектрограмма для 7.mp3
mel_spectrogram = librosa.feature.melspectrogram(y=song7,sr=sample_rate)
log_mel_spectrogram = librosa.power_to_db(mel_spectrogram)
librosa.display.specshow(log_mel_spectrogram, x_axis="time",y_axis="mel", sr=sample_rate)
plt.colorbar(format="%+2.f")

<matplotlib.colorbar.Colorbar at 0x2502016c280>


#mel спектрограмма для 1996.mp3
mel_spectrogram = librosa.feature.melspectrogram(y=song1996,sr=sample_rate)
log_mel_spectrogram = librosa.power_to_db(mel_spectrogram)
librosa.display.specshow(log_mel_spectrogram, x_axis="time",y_axis="mel", sr=sample_rate)
plt.colorbar(format="%+2.f")

<matplotlib.colorbar.Colorbar at 0x250404b3400>


#mel спектрограмма для 2.mp3
mel_spectrogram = librosa.feature.melspectrogram(y=song2,sr=sample_rate)
log_mel_spectrogram = librosa.power_to_db(mel_spectrogram)
librosa.display.specshow(log_mel_spectrogram, x_axis="time",y_axis="mel", sr=sample_rate)
plt.colorbar(format="%+2.f")

<matplotlib.colorbar.Colorbar at 0x2504049e640>


#mel спектрограмма для 19.mp3
mel_spectrogram = librosa.feature.melspectrogram(y=song19,sr=sample_rate)
log_mel_spectrogram = librosa.power_to_db(mel_spectrogram)
librosa.display.specshow(log_mel_spectrogram, x_axis="time",y_axis="mel", sr=sample_rate)
plt.colorbar(format="%+2.f")

<matplotlib.colorbar.Colorbar at 0x2501ea2c9d0>


df_static_averaged_1_2000.head(20)

	sample_15000ms	sample_15500ms	sample_16000ms	sample_16500ms	sample_17000ms	sample_17500ms	sample_18000ms	sample_18500ms	sample_19000ms	sample_19500ms	...	sample_40000ms	sample_40500ms	sample_41000ms	sample_41500ms	sample_42000ms	sample_42500ms	sample_43000ms	sample_43500ms	sample_44000ms	sample_44500ms
0	0.069565	0.069565	0.069565	0.069565	0.069565	0.069565	0.069565	0.069565	0.069565	0.069565	...	0.013043	0.013043	0.013043	0.013043	0.013043	0.013043	0.013043	0.013043	0.013043	0.013043
1	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	...	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780	-0.334780
2	-0.036811	-0.017793	-0.023913	-0.010870	-0.029466	-0.046365	-0.038966	-0.103960	-0.046054	-0.032225	...	0.090484	0.045753	0.028261	0.058696	0.033641	0.026854	0.034225	0.037743	0.038966	0.027969
3	0.418840	0.418840	0.418840	0.418840	0.418840	0.418840	0.418840	0.418840	0.418840	0.418840	...	-0.642570	-0.642570	-0.642570	-0.642570	-0.642570	-0.642570	-0.642570	-0.642570	-0.642570	-0.642570
4	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	...	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390	-0.817390
5	-0.422210	-0.439600	-0.447440	-0.447440	-0.456130	-0.456130	-0.456130	-0.456130	-0.456130	-0.456130	...	-0.438750	-0.426940	-0.436320	-0.459200	-0.468490	-0.472800	-0.482190	-0.489450	-0.511110	-0.521290
6	-0.350000	-0.350000	-0.350000	-0.350000	-0.350000	-0.350000	-0.350000	-0.350000	-0.350000	-0.350000	...	-0.358700	-0.358700	-0.358700	-0.358700	-0.358700	-0.358700	-0.358700	-0.358700	-0.358700	-0.358700
7	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	...	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700	0.508700
8	0.047826	0.056522	0.056522	0.056522	0.056522	0.006003	-0.037964	-0.051545	-0.052174	-0.052174	...	-0.258610	-0.252170	-0.252170	-0.252170	-0.252170	-0.245880	-0.241520	-0.230860	-0.224140	-0.215460
9	-0.177600	-0.243480	-0.244230	-0.279280	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430	...	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430	-0.330430

	sample_15000ms	sample_15500ms	sample_16000ms	sample_16500ms	sample_17000ms	sample_17500ms	sample_18000ms	sample_18500ms	sample_19000ms	sample_19500ms	...	sample_40000ms	sample_40500ms	sample_41000ms	sample_41500ms	sample_42000ms	sample_42500ms	sample_43000ms	sample_43500ms	sample_44000ms	sample_44500ms
0	-0.039130	-0.039130	-0.039130	-0.039130	-0.039130	-0.039130	-0.039130	-0.039130	-0.039130	-0.039130	...	-0.082609	-0.082609	-0.082609	-0.091304	-0.091304	-0.091304	-0.091304	-0.091304	-0.091304	-0.091304
1	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	...	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740	-0.521740
2	-0.384860	-0.399610	-0.402170	-0.402170	-0.402170	-0.389840	-0.405020	-0.390790	-0.375680	-0.390280	...	-0.493480	-0.489130	-0.469510	-0.478000	-0.484780	-0.476090	-0.467590	-0.471740	-0.485570	-0.489130
3	0.088233	0.088233	0.088233	0.088233	0.088233	0.088233	0.088233	0.088233	0.088233	0.088233	...	-0.372870	-0.372870	-0.372870	-0.372870	-0.372870	-0.372870	-0.372870	-0.372870	-0.372870	-0.372870
4	0.834780	0.834780	0.834780	0.834780	0.834780	0.834780	0.834780	0.834780	0.834780	0.834780	...	-0.639130	-0.639130	-0.639130	-0.639130	-0.639130	-0.639130	-0.639130	-0.639130	-0.639130	-0.639130
5	-0.191140	-0.189590	-0.181190	-0.221960	-0.256300	-0.261480	-0.273680	-0.273250	-0.283230	-0.291050	...	-0.386620	-0.390960	-0.390960	-0.395310	-0.395310	-0.395380	-0.404000	-0.404000	-0.404000	-0.391030
6	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	...	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.319560	-0.321260	-0.328260	-0.328260
7	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	...	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480	0.143480
8	-0.173910	-0.173910	-0.173910	-0.173910	-0.173910	-0.173910	-0.173910	-0.173910	-0.173910	-0.173910	...	-0.327520	-0.333280	-0.337580	-0.341900	-0.350560	-0.354840	-0.356520	-0.356520	-0.360870	-0.365870
9	-0.169560	-0.169560	-0.169560	-0.169560	-0.169560	-0.169560	-0.169560	-0.169560	-0.169560	-0.169560	...	-0.173710	-0.242660	-0.256320	-0.264790	-0.269570	-0.273910	-0.282410	-0.282610	-0.282610	-0.282610

	WorkerId	sample_15000ms	sample_15500ms	sample_16000ms	sample_16500ms	sample_17000ms	sample_17500ms	sample_18000ms	sample_18500ms	sample_19000ms	...	sample_40000ms	sample_40500ms	sample_41000ms	sample_41500ms	sample_42000ms	sample_42500ms	sample_43000ms	sample_43500ms	sample_44000ms	sample_44500ms
0	46a2289decf79f747406fa91cd92fc27	0.22	0.10	-0.07	-0.07	-0.07	-0.07	-0.07	-0.07	-0.07	...	-0.08	-0.08	-0.08	-0.08	-0.08	-0.08	-0.08	-0.08	-0.08	-0.08
1	78b5e9744073532cc376976b5fc6b2fc	-0.10	-0.06	-0.02	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	...	0.47	0.47	0.47	0.47	0.47	0.47	0.47	0.47	0.47	0.47
2	b09a5957e5d5e47e556d203529a0ae6d	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.08	...	0.36	0.37	0.38	0.38	0.38	0.38	0.39	0.39	0.39	0.39
3	00de940f0b5cfc82cca4791199e3bfb3	0.32	0.32	0.32	0.32	0.32	0.32	0.32	0.32	0.32	...	0.87	0.89	0.89	0.91	0.92	0.93	0.96	1.00	1.00	1.00
4	accbf566ae920d6260d28454e1ee0d6a	-0.55	-0.55	-0.55	-0.55	-0.55	-0.55	-0.55	-0.55	-0.55	...	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.31	0.52	0.59
5	de2b2c35312ac2f0a8510743742c0219	0.04	0.03	0.03	0.00	0.01	0.01	0.03	0.02	-0.00	...	0.90	0.90	0.92	0.95	0.96	0.92	0.95	0.91	0.88	0.94
6	607f6e34a0b5923333f6b16d3a59cc98	0.34	0.36	0.35	0.34	0.32	0.28	0.28	0.30	0.30	...	0.45	0.47	0.44	0.45	0.46	0.41	0.50	0.49	0.50	0.48
7	ff9c1993d2a21f2117c30d8e295dd4ac	-0.50	-0.50	-0.50	-0.50	-0.50	-0.50	-0.50	-0.50	-0.50	...	0.43	0.43	0.43	0.43	0.43	0.43	0.43	0.43	0.43	0.43
8	ed7ed76453bd846859f5e6b9149df276	0.20	0.20	0.20	0.21	0.21	0.25	0.29	0.30	0.30	...	0.49	0.49	0.49	0.49	0.49	0.49	0.49	0.49	0.49	0.49
9	a30d244141cb2f51e0803e79bc4bd147	0.67	0.67	0.67	0.67	0.67	0.67	0.67	0.67	0.67	...	0.67	0.67	0.67	0.67	0.67	0.67	0.67	0.67	0.67	0.67

	WorkerId	sample_15000ms	sample_15500ms	sample_16000ms	sample_16500ms	sample_17000ms	sample_17500ms	sample_18000ms	sample_18500ms	sample_19000ms	...	sample_40000ms	sample_40500ms	sample_41000ms	sample_41500ms	sample_42000ms	sample_42500ms	sample_43000ms	sample_43500ms	sample_44000ms	sample_44500ms
0	46a2289decf79f747406fa91cd92fc27	0.11	0.11	0.12	0.14	0.15	0.15	0.15	0.15	0.15	...	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.24
1	78b5e9744073532cc376976b5fc6b2fc	0.57	0.57	0.57	0.57	0.57	0.57	0.57	0.57	0.57	...	0.57	0.55	0.46	0.46	0.46	0.46	0.46	0.46	0.47	0.56
2	b09a5957e5d5e47e556d203529a0ae6d	0.11	0.11	0.11	0.11	0.13	0.15	0.17	0.18	0.18	...	0.25	0.25	0.25	0.25	0.25	0.25	0.25	0.25	0.27	0.31
3	00de940f0b5cfc82cca4791199e3bfb3	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	...	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
4	accbf566ae920d6260d28454e1ee0d6a	0.43	0.43	0.43	0.43	0.43	0.43	0.43	0.43	0.43	...	0.66	0.66	0.66	0.55	0.55	0.55	0.59	0.63	0.63	0.75
5	de2b2c35312ac2f0a8510743742c0219	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.03	...	0.92	0.94	0.94	0.92	0.93	0.92	0.91	0.95	0.93	0.92
6	607f6e34a0b5923333f6b16d3a59cc98	0.26	0.26	0.27	0.28	0.28	0.28	0.28	0.29	0.28	...	0.41	0.41	0.41	0.38	0.43	0.39	0.49	0.46	0.47	0.44
7	ff9c1993d2a21f2117c30d8e295dd4ac	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	...	-0.33	-0.33	-0.33	-0.33	-0.33	-0.33	-0.33	-0.33	-0.33	-0.33
8	ed7ed76453bd846859f5e6b9149df276	0.48	0.48	0.48	0.48	0.48	0.48	0.48	0.48	0.48	...	0.53	0.53	0.53	0.53	0.53	0.53	0.53	0.53	0.53	0.53
9	a30d244141cb2f51e0803e79bc4bd147	0.69	0.69	0.69	0.69	0.69	0.69	0.69	0.69	0.69	...	0.69	0.69	0.69	0.69	0.69	0.69	0.69	0.69	0.69	0.69

	WorkerId	sample_15000ms	sample_15500ms	sample_16000ms	sample_16500ms	sample_17000ms	sample_17500ms	sample_18000ms	sample_18500ms	sample_19000ms	...	sample_296000ms	sample_296500ms	sample_297000ms	sample_297500ms	sample_298000ms	sample_298500ms	sample_299000ms	sample_299500ms	sample_300000ms	sample_300500ms
0	883449004b1bf2a07a284f59ddae1fd7	0.10	0.10	0.10	0.10	0.07	0.04	0.04	0.04	0.04	...	-0.63	-0.64	-0.64	-0.62	-0.59	-0.52	-0.49	-0.48	-0.43	-0.43
1	b09a5957e5d5e47e556d203529a0ae6d	-0.07	-0.08	-0.08	-0.08	-0.08	-0.08	-0.10	-0.13	-0.16	...	-0.87	-0.87	-0.87	-0.87	-0.87	-0.87	-0.87	-0.87	-0.87	-0.87
2	a8d14f76676af36b8978406be47c0c38	0.07	0.07	0.07	0.07	0.08	0.08	0.08	0.08	0.08	...	-0.43	-0.41	-0.39	-0.34	-0.28	-0.23	-0.15	-0.12	-0.10	-0.06
3	02bf7a99a5e47f4d52939ab7efc8a549	-0.60	-0.60	-0.60	-0.57	-0.54	-0.54	-0.49	-0.49	-0.46	...	-0.75	-0.94	-0.95	-0.98	-0.96	-0.96	-0.96	-0.82	-0.66	-0.63
4	467b1ff3ccfff51e2882bd9d39ef2082	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	...	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	0.00	0.00	0.00

	workerID	SongId	Valence	Arousal
0	6010bbc8e7ef4b21fa38f9c3a9754ef3	2	5	2
1	3c888e77b992ae3cd2adfe16774e23b9	2	2	3
2	2afd218c3aecb6828d2be327f8b9c46f	2	3	3
3	fd5b08ce362d855ca9152a894348130c	2	4	4
4	9c8073214a052e414811b76012df8847	2	2	2
...	...	...	...	...
17459	607f6e34a0b5923333f6b16d3a59cc98	2000	6	5
17460	78b5e9744073532cc376976b5fc6b2fc	2000	7	7
17461	7cecbffe1da5ae974952db6c13695afe	2000	4	5
17462	ed7ed76453bd846859f5e6b9149df276	2000	6	7
17463	b09a5957e5d5e47e556d203529a0ae6d	2000	6	7

	workerID	SongId	Valence	Arousal
7554	46a2289decf79f747406fa91cd92fc27	1010	5	5
7555	78b5e9744073532cc376976b5fc6b2fc	1010	7	7
7556	b09a5957e5d5e47e556d203529a0ae6d	1010	6	6
7557	00de940f0b5cfc82cca4791199e3bfb3	1010	9	9
7558	accbf566ae920d6260d28454e1ee0d6a	1010	8	7
7559	de2b2c35312ac2f0a8510743742c0219	1010	9	9
7560	607f6e34a0b5923333f6b16d3a59cc98	1010	6	5
7561	ff9c1993d2a21f2117c30d8e295dd4ac	1010	4	5
7562	ed7ed76453bd846859f5e6b9149df276	1010	5	5
7563	a30d244141cb2f51e0803e79bc4bd147	1010	6	7

	workerID	SongId	Valence	Arousal
2730	6010bbc8e7ef4b21fa38f9c3a9754ef3	323	7	6
2731	bf34e3c5724ce07d29ef12db5f767258	323	4	6
2732	24ebcf86498b0f2793b55c5b9a7756b5	323	5	4
2733	38531641e6c0628757776b0088bcc854	323	6	6
2734	2afd218c3aecb6828d2be327f8b9c46f	323	3	5
2735	fd5b08ce362d855ca9152a894348130c	323	6	4
2736	9c8073214a052e414811b76012df8847	323	6	7
2737	64cae7b86c7dcb1b40d17e43c0c2109e	323	3	3
2738	64cae7b86c7dcb1b40d17e43c0c2109e	323	4	8
2739	259443e9cad56b4f68b664daa20cb323	323	3	7
2740	490951556961e4b88fe1a3ac53b3b186	323	6	7

	SongId	WorkerId	Arousal_Average	Valence_Average	Arousal_Maximum	Valence_Maximum	Arousal_Minimum	Valence_Minimum
0	2001	523edc3414996c8e52eb14f20c93fe96	7	3	8	7	2	2
1	2001	ae0b751438f08bb474e5cd3e48e0d417	5	3	9	3	3	2
2	2001	fd4cc3ae7434a18286204f133b490502	7	3	8	5	4	2
3	2001	58af5138dd2f00af1f52d2c2c031f4a2	7	2	9	4	5	1
4	2001	3c960e4e5a02213fc82b98b770a0663c	7	5	9	6	3	4

	id	Filename	title	artist	album	genre
0	2001	Creepoid_OldTree	Old Tree	Creepoid	NaN	Rock
1	2002	AmarLal_SpringDay1	Spring Day 1	Amar Lal	NaN	Singer/Songwriter
2	2003	JoelHelander_ExcessiveResistancetoChange	Excessive Resistance to Change	Joel Helander	NaN	Classical
3	2004	AimeeNorwich_Flying	Flying	Aimee Norwich	NaN	World/Folk
4	2005	AvaLuna_Waterduct	Waterduct	Ava Luna	NaN	Rock

	valence_mean	valence_std	arousal_mean	arousal_std	class
song_id
2	3.1	0.94	3.0	0.63	3
3	3.5	1.75	3.3	1.62	3
4	5.7	1.42	5.5	1.63	1
5	4.4	2.01	5.3	1.85	2
7	5.8	1.47	6.4	1.69	1

	valence_mean	valence_std	valence_ max_mean	valence_max_std	valence_min_mean	valence_min_std	arousal_mean	arousal_std	arousal_max_mean	arousal_max_std	arousal_min_mean	arousal_min_std	class
song_id
2001	3.2	0.98	5.0	1.41	2.2	0.98	6.6	0.80	8.6	0.49	3.4	1.02	2
2002	6.4	0.49	8.2	0.98	5.0	1.10	5.2	1.17	7.4	1.36	2.2	1.17	1
2003	5.4	1.50	7.2	1.17	3.4	1.02	4.6	1.85	6.2	2.04	1.4	0.49	4
2004	5.0	1.10	6.4	1.02	3.2	1.17	4.8	1.60	6.0	2.28	2.8	0.98	3
2005	3.8	1.17	5.0	1.10	1.6	0.80	5.2	0.75	8.4	0.80	2.0	1.10	2